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TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation
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We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline. In the proposed pipeline, multiple large language model (LLM) agents are created under various roles with specialized prompts and access to different parts of information, and the chat data is acquired by logging the conversation between the Listener LLM and the Recsys LLM. To cover various conversation scenarios, for each conversation, the Listener LLM is conditioned on a finetuned conversation goal. Finally, all the LLMs are multimodal with audio and images, allowing a simulation of multimodal recommendation and conversation. In the LLM-as-a-judge and subjective evaluation experiments, TalkPlayData 2 achieved the proposed goal in various aspects related to training a generative recommendation model for music. TalkPlayData 2 and its generation code are released at https://talkpl-ai.github.io.
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Cited by 4 Pith papers
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LLM-as-a-Judge for Evaluating System Responses in Conversational Music Recommendation
LLM judges agree moderately with human experts when scoring conversational music recommendation responses, outperform reference-based metrics, but are not reliable enough to replace human evaluation.
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Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems
Agentic recommender systems are organized by agent role (assisted, as-recommender, as-simulator) crossed with autonomy levels L2–L5, yielding a roadmap of architectures, evaluation limits, and open challenges.
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TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling
An LLM that plans tool calls — SQL, BM25, dense, and semantic-ID retrieval — yields small Hit@K gains over BM25-style baselines for conversational music recommendation on the synthetic TalkPlayData 2 benchmark.
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Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation
A review and position paper proposing a six-dimension success framework and risk diagnostics for evaluating LLM-based music recommendation systems.
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